{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第七章 搭建一个带评估的端到端问答系统"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在本节课中，我们将搭建一个带评估的端到端问答系统，综合了之前多节课的内容，加入了评估过程。\n",
    "\n",
    "首先，我们将检查输入，看看它是否能够通过审核 API 的审核。\n",
    "\n",
    "其次，如果没有，我们将提取产品列表。\n",
    "\n",
    "第三，如果找到了产品，我们将尝试查找它们。\n",
    "\n",
    "第四，我们将使用模型回答用户问题。\n",
    "\n",
    "最后，我们将通过审核API对答案进行审核。\n",
    "\n",
    "如果没有被标记，我们将把答案返回给用户。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "环境配置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
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   "source": [
    "# 配置 OpenAI KEY\n",
    "import os\n",
    "import openai\n",
    "import sys\n",
    "sys.path.append('../..')\n",
    "# 使用英文 Prompt 的工具包\n",
    "import utils_en\n",
    "# 使用中文 Prompt 的工具包\n",
    "import utils_zh\n",
    "\n",
    "import panel as pn  # 用于图形化界面\n",
    "pn.extension()\n",
    "\n",
    "from dotenv import load_dotenv, find_dotenv\n",
    "_ = load_dotenv(find_dotenv()) # read local .env file\n",
    "\n",
    "openai.api_key  = os.environ['OPENAI_API_KEY']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 封装一个访问 OpenAI GPT3.5 的函数\n",
    "def get_completion_from_messages(messages, model=\"gpt-3.5-turbo\", temperature=0, max_tokens=500):\n",
    "    response = openai.ChatCompletion.create(\n",
    "        model=model,\n",
    "        messages=messages,\n",
    "        temperature=temperature, \n",
    "        max_tokens=max_tokens, \n",
    "    )\n",
    "    return response.choices[0].message[\"content\"]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一个端到端实现问答的函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第一步：输入通过 Moderation 检查\n",
      "第二步：抽取出商品列表\n",
      "第三步：查找抽取出的商品信息\n",
      "第四步：生成用户回答\n",
      "第五步：输出经过 Moderation 检查\n",
      "第六步：模型评估该回答\n",
      "第七步：模型赞同了该回答.\n",
      "The SmartX ProPhone is a powerful smartphone with a 6.1-inch display, 128GB storage, 12MP dual camera, and 5G capabilities. The FotoSnap DSLR Camera is a versatile camera with a 24.2MP sensor, 1080p video, 3-inch LCD, and interchangeable lenses. As for our TVs, we have a range of options including the CineView 4K TV with a 55-inch display, 4K resolution, HDR, and smart TV capabilities, the CineView 8K TV with a 65-inch display, 8K resolution, HDR, and smart TV capabilities, and the CineView OLED TV with a 55-inch display, 4K resolution, HDR, and smart TV capabilities. Do you have any specific questions about these products or would you like me to recommend a product based on your needs?\n"
     ]
    }
   ],
   "source": [
    "# 对用户信息进行预处理\n",
    "def process_user_message(user_input, all_messages, debug=True):\n",
    "    # user_input : 用户输入\n",
    "    # all_messages : 历史信息\n",
    "    # debug : 是否开启 DEBUG 模式,默认开启\n",
    "\n",
    "    # 分隔符\n",
    "    delimiter = \"```\"\n",
    "    \n",
    "    # 第一步: 使用 OpenAI 的 Moderation API 检查用户输入是否合规或者是一个注入的 Prompt\n",
    "    response = openai.Moderation.create(input=user_input)\n",
    "    moderation_output = response[\"results\"][0]\n",
    "\n",
    "    # 经过 Moderation API 检查该输入不合规\n",
    "    if moderation_output[\"flagged\"]:\n",
    "        print(\"第一步：输入被 Moderation 拒绝\")\n",
    "        return \"抱歉，您的请求不合规\"\n",
    "\n",
    "    # 如果开启了 DEBUG 模式，打印实时进度\n",
    "    if debug: print(\"第一步：输入通过 Moderation 检查\")\n",
    "    \n",
    "    # 第二步：抽取出商品和对应的目录，类似于之前课程中的方法，做了一个封装\n",
    "    category_and_product_response = utils_en.find_category_and_product_only(user_input, utils_en.get_products_and_category())\n",
    "    #print(category_and_product_response)\n",
    "    # 将抽取出来的字符串转化为列表\n",
    "    category_and_product_list = utils_en.read_string_to_list(category_and_product_response)\n",
    "    #print(category_and_product_list)\n",
    "\n",
    "    if debug: print(\"第二步：抽取出商品列表\")\n",
    "\n",
    "    # 第三步：查找商品对应信息\n",
    "    product_information = utils_en.generate_output_string(category_and_product_list)\n",
    "    if debug: print(\"第三步：查找抽取出的商品信息\")\n",
    "\n",
    "    # 第四步：根据信息生成回答\n",
    "    system_message = f\"\"\"\n",
    "    You are a customer service assistant for a large electronic store. \\\n",
    "    Respond in a friendly and helpful tone, with concise answers. \\\n",
    "    Make sure to ask the user relevant follow-up questions.\n",
    "    \"\"\"\n",
    "    # 插入 message\n",
    "    messages = [\n",
    "        {'role': 'system', 'content': system_message},\n",
    "        {'role': 'user', 'content': f\"{delimiter}{user_input}{delimiter}\"},\n",
    "        {'role': 'assistant', 'content': f\"Relevant product information:\\n{product_information}\"}\n",
    "    ]\n",
    "    # 获取 GPT3.5 的回答\n",
    "    # 通过附加 all_messages 实现多轮对话\n",
    "    final_response = get_completion_from_messages(all_messages + messages)\n",
    "    if debug:print(\"第四步：生成用户回答\")\n",
    "    # 将该轮信息加入到历史信息中\n",
    "    all_messages = all_messages + messages[1:]\n",
    "\n",
    "    # 第五步：基于 Moderation API 检查输出是否合规\n",
    "    response = openai.Moderation.create(input=final_response)\n",
    "    moderation_output = response[\"results\"][0]\n",
    "\n",
    "    # 输出不合规\n",
    "    if moderation_output[\"flagged\"]:\n",
    "        if debug: print(\"第五步：输出被 Moderation 拒绝\")\n",
    "        return \"抱歉，我们不能提供该信息\"\n",
    "\n",
    "    if debug: print(\"第五步：输出经过 Moderation 检查\")\n",
    "\n",
    "    # 第六步：模型检查是否很好地回答了用户问题\n",
    "    user_message = f\"\"\"\n",
    "    Customer message: {delimiter}{user_input}{delimiter}\n",
    "    Agent response: {delimiter}{final_response}{delimiter}\n",
    "\n",
    "    Does the response sufficiently answer the question?\n",
    "    \"\"\"\n",
    "    messages = [\n",
    "        {'role': 'system', 'content': system_message},\n",
    "        {'role': 'user', 'content': user_message}\n",
    "    ]\n",
    "    # 要求模型评估回答\n",
    "    evaluation_response = get_completion_from_messages(messages)\n",
    "    if debug: print(\"第六步：模型评估该回答\")\n",
    "\n",
    "    # 第七步：如果评估为 Y，输出回答；如果评估为 N，反馈将由人工修正答案\n",
    "    if \"Y\" in evaluation_response:  # 使用 in 来避免模型可能生成 Yes\n",
    "        if debug: print(\"第七步：模型赞同了该回答.\")\n",
    "        return final_response, all_messages\n",
    "    else:\n",
    "        if debug: print(\"第七步：模型不赞成该回答.\")\n",
    "        neg_str = \"很抱歉，我无法提供您所需的信息。我将为您转接到一位人工客服代表以获取进一步帮助。\"\n",
    "        return neg_str, all_messages\n",
    "\n",
    "user_input = \"tell me about the smartx pro phone and the fotosnap camera, the dslr one. Also what tell me about your tvs\"\n",
    "response,_ = process_user_message(user_input,[])\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第一步：输入通过 Moderation 检查\n",
      "第二步：抽取出商品列表\n",
      "第三步：查找抽取出的商品信息\n",
      "第四步：生成用户回答\n",
      "第五步：输出经过 Moderation 检查\n",
      "第六步：模型评估该回答\n",
      "第七步：模型赞同了该回答.\n",
      "关于SmartX ProPhone和FotoSnap相机的信息：\n",
      "\n",
      "SmartX ProPhone是一款功能强大的智能手机，具有6.1英寸的显示屏，128GB的存储空间，12MP的双摄像头和5G网络。售价为899.99美元。\n",
      "\n",
      "FotoSnap相机系列包括DSLR相机、无反相机和即时相机。DSLR相机具有24.2MP传感器、1080p视频、3英寸LCD和可更换镜头。无反相机具有20.1MP传感器、4K视频、3英寸触摸屏和可更换镜头。即时相机可以即时打印照片，具有内置闪光灯、自拍镜和电池供电。售价分别为599.99美元、799.99美元和69.99美元。\n",
      "\n",
      "关于我们的电视：\n",
      "\n",
      "我们有多种电视可供选择，包括CineView 4K电视、CineView 8K电视和CineView OLED电视。CineView 4K电视具有55英寸的显示屏、4K分辨率、HDR和智能电视功能。CineView 8K电视具有65英寸的显示屏、8K分辨率、HDR和智能电视功能。CineView OLED电视具有55英寸的显示屏、4K分辨率、HDR和智能电视功能。我们还提供SoundMax家庭影院和SoundMax声音栏，以提供更好的音频体验。售价从199.99美元到2999.99美元不等，保修期为1年或2年。\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "中文Prompt\n",
    "注意：限于模型对中文理解能力较弱，中文Prompt可能会随机出现不成功，可以多次运行；也非常欢迎同学探究更稳定的中文 Prompt\n",
    "'''\n",
    "# 对用户信息进行预处理\n",
    "def process_user_message_ch(user_input, all_messages, debug=True):\n",
    "    # user_input : 用户输入\n",
    "    # all_messages : 历史信息\n",
    "    # debug : 是否开启 DEBUG 模式,默认开启\n",
    "\n",
    "    # 分隔符\n",
    "    delimiter = \"```\"\n",
    "    \n",
    "    # 第一步: 使用 OpenAI 的 Moderation API 检查用户输入是否合规或者是一个注入的 Prompt\n",
    "    response = openai.Moderation.create(input=user_input)\n",
    "    moderation_output = response[\"results\"][0]\n",
    "\n",
    "    # 经过 Moderation API 检查该输入不合规\n",
    "    if moderation_output[\"flagged\"]:\n",
    "        print(\"第一步：输入被 Moderation 拒绝\")\n",
    "        return \"抱歉，您的请求不合规\"\n",
    "\n",
    "    # 如果开启了 DEBUG 模式，打印实时进度\n",
    "    if debug: print(\"第一步：输入通过 Moderation 检查\")\n",
    "    \n",
    "    # 第二步：抽取出商品和对应的目录，类似于之前课程中的方法，做了一个封装\n",
    "    category_and_product_response = utils_zh.find_category_and_product_only(user_input, utils_zh.get_products_and_category())\n",
    "    #print(category_and_product_response)\n",
    "    # 将抽取出来的字符串转化为列表\n",
    "    category_and_product_list = utils_zh.read_string_to_list(category_and_product_response)\n",
    "    #print(category_and_product_list)\n",
    "\n",
    "    if debug: print(\"第二步：抽取出商品列表\")\n",
    "\n",
    "    # 第三步：查找商品对应信息\n",
    "    product_information = utils_zh.generate_output_string(category_and_product_list)\n",
    "    if debug: print(\"第三步：查找抽取出的商品信息\")\n",
    "\n",
    "    # 第四步：根据信息生成回答\n",
    "    system_message = f\"\"\"\n",
    "        您是一家大型电子商店的客户服务助理。\\\n",
    "        请以友好和乐于助人的语气回答问题，并提供简洁明了的答案。\\\n",
    "        请确保向用户提出相关的后续问题。\n",
    "    \"\"\"\n",
    "    # 插入 message\n",
    "    messages = [\n",
    "        {'role': 'system', 'content': system_message},\n",
    "        {'role': 'user', 'content': f\"{delimiter}{user_input}{delimiter}\"},\n",
    "        {'role': 'assistant', 'content': f\"相关商品信息:\\n{product_information}\"}\n",
    "    ]\n",
    "    # 获取 GPT3.5 的回答\n",
    "    # 通过附加 all_messages 实现多轮对话\n",
    "    final_response = get_completion_from_messages(all_messages + messages)\n",
    "    if debug:print(\"第四步：生成用户回答\")\n",
    "    # 将该轮信息加入到历史信息中\n",
    "    all_messages = all_messages + messages[1:]\n",
    "\n",
    "    # 第五步：基于 Moderation API 检查输出是否合规\n",
    "    response = openai.Moderation.create(input=final_response)\n",
    "    moderation_output = response[\"results\"][0]\n",
    "\n",
    "    # 输出不合规\n",
    "    if moderation_output[\"flagged\"]:\n",
    "        if debug: print(\"第五步：输出被 Moderation 拒绝\")\n",
    "        return \"抱歉，我们不能提供该信息\"\n",
    "\n",
    "    if debug: print(\"第五步：输出经过 Moderation 检查\")\n",
    "\n",
    "    # 第六步：模型检查是否很好地回答了用户问题\n",
    "    user_message = f\"\"\"\n",
    "    用户信息: {delimiter}{user_input}{delimiter}\n",
    "    代理回复: {delimiter}{final_response}{delimiter}\n",
    "\n",
    "    回复是否足够回答问题\n",
    "    如果足够，回答 Y\n",
    "    如果不足够，回答 N\n",
    "    仅回答上述字母即可\n",
    "    \"\"\"\n",
    "    # print(final_response)\n",
    "    messages = [\n",
    "        {'role': 'system', 'content': system_message},\n",
    "        {'role': 'user', 'content': user_message}\n",
    "    ]\n",
    "    # 要求模型评估回答\n",
    "    evaluation_response = get_completion_from_messages(messages)\n",
    "    # print(evaluation_response)\n",
    "    if debug: print(\"第六步：模型评估该回答\")\n",
    "\n",
    "    # 第七步：如果评估为 Y，输出回答；如果评估为 N，反馈将由人工修正答案\n",
    "    if \"Y\" in evaluation_response:  # 使用 in 来避免模型可能生成 Yes\n",
    "        if debug: print(\"第七步：模型赞同了该回答.\")\n",
    "        return final_response, all_messages\n",
    "    else:\n",
    "        if debug: print(\"第七步：模型不赞成该回答.\")\n",
    "        neg_str = \"很抱歉，我无法提供您所需的信息。我将为您转接到一位人工客服代表以获取进一步帮助。\"\n",
    "        return neg_str, all_messages\n",
    "\n",
    "user_input = \"请告诉我关于smartx pro phone和the fotosnap camera的信息。另外，请告诉我关于你们的tvs的情况。\"\n",
    "response,_ = process_user_message_ch(user_input,[])\n",
    "print(response)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "实现一个可视化界面"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def collect_messages_en(debug=False):\n",
    "    user_input = inp.value_input\n",
    "    if debug: print(f\"User Input = {user_input}\")\n",
    "    if user_input == \"\":\n",
    "        return\n",
    "    inp.value = ''\n",
    "    global context\n",
    "    # 调用 process_user_message 函数\n",
    "    #response, context = process_user_message(user_input, context, utils.get_products_and_category(),debug=True)\n",
    "    response, context = process_user_message(user_input, context, debug=False)\n",
    "    context.append({'role':'assistant', 'content':f\"{response}\"})\n",
    "    panels.append(\n",
    "        pn.Row('User:', pn.pane.Markdown(user_input, width=600)))\n",
    "    panels.append(\n",
    "        pn.Row('Assistant:', pn.pane.Markdown(response, width=600, style={'background-color': '#F6F6F6'})))\n",
    " \n",
    "    return pn.Column(*panels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 调用中文Prompt版本\n",
    "def collect_messages_ch(debug=False):\n",
    "    user_input = inp.value_input\n",
    "    if debug: print(f\"User Input = {user_input}\")\n",
    "    if user_input == \"\":\n",
    "        return\n",
    "    inp.value = ''\n",
    "    global context\n",
    "    # 调用 process_user_message 函数\n",
    "    #response, context = process_user_message(user_input, context, utils.get_products_and_category(),debug=True)\n",
    "    response, context = process_user_message_ch(user_input, context, debug=False)\n",
    "    context.append({'role':'assistant', 'content':f\"{response}\"})\n",
    "    panels.append(\n",
    "        pn.Row('User:', pn.pane.Markdown(user_input, width=600)))\n",
    "    panels.append(\n",
    "        pn.Row('Assistant:', pn.pane.Markdown(response, width=600, style={'background-color': '#F6F6F6'})))\n",
    " \n",
    "    return pn.Column(*panels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "Column\n",
       "    [0] TextInput(placeholder='Enter text here…')\n",
       "    [1] Row\n",
       "        [0] Button(name='Service Assistant')\n",
       "    [2] ParamFunction(function, _pane=Str, height=300, loading_indicator=True)"
      ]
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   ],
   "source": [
    "panels = [] # collect display \n",
    "\n",
    "# 系统信息\n",
    "context = [ {'role':'system', 'content':\"You are Service Assistant\"} ]  \n",
    "\n",
    "inp = pn.widgets.TextInput( placeholder='Enter text here…')\n",
    "button_conversation = pn.widgets.Button(name=\"Service Assistant\")\n",
    "\n",
    "interactive_conversation = pn.bind(collect_messages, button_conversation)\n",
    "\n",
    "dashboard = pn.Column(\n",
    "    inp,\n",
    "    pn.Row(button_conversation),\n",
    "    pn.panel(interactive_conversation, loading_indicator=True, height=300),\n",
    ")\n",
    "\n",
    "dashboard"
   ]
  },
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   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过监控系统在更多输入上的质量，您可以修改步骤，提高系统的整体性能。\n",
    "\n",
    "也许我们会发现，对于某些步骤，我们的提示可能更好，也许有些步骤甚至不必要，也许我们会找到更好的检索方法等等。\n",
    "\n",
    "我们将在下一个视频中进一步讨论这个问题。 "
   ]
  }
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